ML-Aware Synthetic Data Generation
S. Tsimfer · 2022
Summary Seismic interpretation automation has been on the rise for the last few years: new emerging technologies, especially deep neural networks, have been successfully applied for detection numerous types of structural and geological patterns. Unfortunately, the quality of such models directly depends on the quality and amount of training data. While historic and archive fields may provide enough material for prototypes and hypothesis testing, it is nowhere near the needed amount of data for truly production-ready algorithms. To alleviate this problem, we’ve developed a synthetic generator, targeted specifically at model training. Despite being based on trivial assumptions and physical models, it uses a number of techniques to improve variation of created images, while keeping them look alike to real seismic surveys. With its help, we’ve been able to enhance the performance of our fault and horizon tracking models, as well as to tackle the seismic acoustic inversion task.